Key points are not available for this paper at this time.
This scoping review focuses on the evolution of pre-analytical errors (PAEs) in medical laboratories, a critical area with significant implications for patient care, healthcare costs, hospital length of stay, and operational efficiency. The Covidence Review tool was used to formulate the keywords, and then a comprehensive literature search was performed using several databases, importing the search results directly into Covidence (n=379). Title, abstract screening, duplicate removal, and full-text screening were done. The retrieved studies (n=232) were scanned for eligibility (n=228) and included in the review (n=83), and the results were summarised in a PRISMA flow chart. The review highlights the role of healthcare professionals in preventing PAEs in specimen collection and processing, as well as analyses. The review also discusses the use and advancements of artificial intelligence (AI) and machine learning in reducing PAEs and identifies inadequacies in standard definitions, measurement units, and education strategies. It demonstrates the need for further research to ensure model validation, address the regulatory validation of Risk Probability Indexation (RPI) models and consider regulatory, safety, and privacy concerns. The review suggests that comprehensive studies on the effectiveness of AI and software platforms in real-world settings and their implementation in healthcare are lacking, presenting opportunities for further research to advance patient care and improve the management of PAEs.
John et al. (Tue,) studied this question.